Overview

Dataset statistics

Number of variables24
Number of observations991320
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory189.1 MiB
Average record size in memory200.0 B

Variable types

Categorical4
Numeric19
Boolean1

Alerts

height is highly overall correlated with weight and 2 other fieldsHigh correlation
weight is highly overall correlated with height and 3 other fieldsHigh correlation
waistline is highly overall correlated with weightHigh correlation
sight_left is highly overall correlated with sight_rightHigh correlation
sight_right is highly overall correlated with sight_leftHigh correlation
SBP is highly overall correlated with DBPHigh correlation
DBP is highly overall correlated with SBPHigh correlation
tot_chole is highly overall correlated with LDL_choleHigh correlation
LDL_chole is highly overall correlated with tot_choleHigh correlation
hemoglobin is highly overall correlated with height and 2 other fieldsHigh correlation
SGOT_AST is highly overall correlated with SGOT_ALTHigh correlation
SGOT_ALT is highly overall correlated with SGOT_AST and 1 other fieldsHigh correlation
gamma_GTP is highly overall correlated with SGOT_ALTHigh correlation
sex is highly overall correlated with height and 3 other fieldsHigh correlation
hear_left is highly overall correlated with hear_rightHigh correlation
hear_right is highly overall correlated with hear_leftHigh correlation
SMK_stat_type_cd is highly overall correlated with sexHigh correlation
hear_left is highly imbalanced (79.8%)Imbalance
hear_right is highly imbalanced (80.3%)Imbalance
waistline is highly skewed (γ1 = 26.7893172)Skewed
HDL_chole is highly skewed (γ1 = 104.5785515)Skewed
serum_creatinine is highly skewed (γ1 = 111.0212)Skewed
SGOT_AST is highly skewed (γ1 = 150.4902033)Skewed
SGOT_ALT is highly skewed (γ1 = 50.0384038)Skewed

Reproduction

Analysis started2023-12-18 20:26:54.537540
Analysis finished2023-12-18 20:28:05.224955
Duration1 minute and 10.69 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

sex
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.1 MiB
Male
526399 
Female
464921 

Length

Max length6
Median length4
Mean length4.9379837
Min length4

Characters and Unicode

Total characters4895122
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 526399
53.1%
Female 464921
46.9%

Length

2023-12-18T13:28:05.260533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-18T13:28:05.304852image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
male 526399
53.1%
female 464921
46.9%

Most occurring characters

ValueCountFrequency (%)
e 1456241
29.7%
a 991320
20.3%
l 991320
20.3%
M 526399
 
10.8%
F 464921
 
9.5%
m 464921
 
9.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3903802
79.7%
Uppercase Letter 991320
 
20.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1456241
37.3%
a 991320
25.4%
l 991320
25.4%
m 464921
 
11.9%
Uppercase Letter
ValueCountFrequency (%)
M 526399
53.1%
F 464921
46.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 4895122
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1456241
29.7%
a 991320
20.3%
l 991320
20.3%
M 526399
 
10.8%
F 464921
 
9.5%
m 464921
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4895122
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1456241
29.7%
a 991320
20.3%
l 991320
20.3%
M 526399
 
10.8%
F 464921
 
9.5%
m 464921
 
9.5%

age
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.614529
Minimum20
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 MiB
2023-12-18T13:28:05.335974image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile25
Q135
median45
Q360
95-th percentile70
Maximum85
Range65
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.181346
Coefficient of variation (CV)0.29783654
Kurtosis-0.57561122
Mean47.614529
Median Absolute Deviation (MAD)10
Skewness0.15365228
Sum47201235
Variance201.11059
MonotonicityNot monotonic
2023-12-18T13:28:05.483744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
40 130381
13.2%
50 129430
13.1%
45 118353
11.9%
55 111221
11.2%
60 106062
10.7%
35 84722
8.5%
30 77598
7.8%
25 64369
6.5%
65 52957
5.3%
70 50666
 
5.1%
Other values (4) 65561
6.6%
ValueCountFrequency (%)
20 21970
 
2.2%
25 64369
6.5%
30 77598
7.8%
35 84722
8.5%
40 130381
13.2%
45 118353
11.9%
50 129430
13.1%
55 111221
11.2%
60 106062
10.7%
65 52957
5.3%
ValueCountFrequency (%)
85 3291
 
0.3%
80 14968
 
1.5%
75 25332
 
2.6%
70 50666
 
5.1%
65 52957
5.3%
60 106062
10.7%
55 111221
11.2%
50 129430
13.1%
45 118353
11.9%
40 130381
13.2%

height
Real number (ℝ)

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean162.24056
Minimum130
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 MiB
2023-12-18T13:28:05.523270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum130
5-th percentile145
Q1155
median160
Q3170
95-th percentile175
Maximum190
Range60
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.282922
Coefficient of variation (CV)0.057217023
Kurtosis-0.53563938
Mean162.24056
Median Absolute Deviation (MAD)5
Skewness-0.022721155
Sum1.6083232 × 108
Variance86.172641
MonotonicityNot monotonic
2023-12-18T13:28:05.557552image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
160 181806
18.3%
165 178224
18.0%
170 166319
16.8%
155 165676
16.7%
150 107927
10.9%
175 98849
10.0%
145 39175
 
4.0%
180 35967
 
3.6%
140 9099
 
0.9%
185 6588
 
0.7%
Other values (3) 1690
 
0.2%
ValueCountFrequency (%)
130 86
 
< 0.1%
135 1241
 
0.1%
140 9099
 
0.9%
145 39175
 
4.0%
150 107927
10.9%
155 165676
16.7%
160 181806
18.3%
165 178224
18.0%
170 166319
16.8%
175 98849
10.0%
ValueCountFrequency (%)
190 363
 
< 0.1%
185 6588
 
0.7%
180 35967
 
3.6%
175 98849
10.0%
170 166319
16.8%
165 178224
18.0%
160 181806
18.3%
155 165676
16.7%
150 107927
10.9%
145 39175
 
4.0%

weight
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.283884
Minimum25
Maximum140
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 MiB
2023-12-18T13:28:05.599020image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile45
Q155
median60
Q370
95-th percentile85
Maximum140
Range115
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.514101
Coefficient of variation (CV)0.19774547
Kurtosis0.35920714
Mean63.283884
Median Absolute Deviation (MAD)10
Skewness0.57653665
Sum62734580
Variance156.60273
MonotonicityNot monotonic
2023-12-18T13:28:05.641442image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
60 151132
15.2%
55 150412
15.2%
65 141237
14.2%
50 125076
12.6%
70 122277
12.3%
75 90205
9.1%
45 63046
6.4%
80 58175
 
5.9%
85 33706
 
3.4%
90 18250
 
1.8%
Other values (14) 37804
 
3.8%
ValueCountFrequency (%)
25 9
 
< 0.1%
30 157
 
< 0.1%
35 1948
 
0.2%
40 16639
 
1.7%
45 63046
6.4%
50 125076
12.6%
55 150412
15.2%
60 151132
15.2%
65 141237
14.2%
70 122277
12.3%
ValueCountFrequency (%)
140 3
 
< 0.1%
135 5
 
< 0.1%
130 43
 
< 0.1%
125 80
 
< 0.1%
120 236
 
< 0.1%
115 573
 
0.1%
110 1177
 
0.1%
105 2453
 
0.2%
100 4829
0.5%
95 9652
1.0%

waistline
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct737
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.233255
Minimum8
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 MiB
2023-12-18T13:28:05.691853image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile66
Q174.1
median81
Q387.8
95-th percentile97
Maximum999
Range991
Interquartile range (IQR)13.7

Descriptive statistics

Standard deviation11.850296
Coefficient of variation (CV)0.14587986
Kurtosis2066.8865
Mean81.233255
Median Absolute Deviation (MAD)6.8
Skewness26.789317
Sum80528150
Variance140.4295
MonotonicityNot monotonic
2023-12-18T13:28:05.742439image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 37790
 
3.8%
81 34603
 
3.5%
82 34022
 
3.4%
84 33912
 
3.4%
86 32722
 
3.3%
83 32280
 
3.3%
76 31252
 
3.2%
78 30831
 
3.1%
85 30625
 
3.1%
79 28853
 
2.9%
Other values (727) 664430
67.0%
ValueCountFrequency (%)
8 1
 
< 0.1%
27 1
 
< 0.1%
30 2
< 0.1%
32 3
< 0.1%
35 2
< 0.1%
40 1
 
< 0.1%
42 1
 
< 0.1%
43 1
 
< 0.1%
48 1
 
< 0.1%
49 1
 
< 0.1%
ValueCountFrequency (%)
999 57
< 0.1%
149.1 1
 
< 0.1%
145 1
 
< 0.1%
140 1
 
< 0.1%
138 1
 
< 0.1%
136.8 1
 
< 0.1%
136 2
 
< 0.1%
135 1
 
< 0.1%
134 3
 
< 0.1%
133 1
 
< 0.1%

sight_left
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.98083343
Minimum0.1
Maximum9.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 MiB
2023-12-18T13:28:05.788748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.4
Q10.7
median1
Q31.2
95-th percentile1.5
Maximum9.9
Range9.8
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.60595384
Coefficient of variation (CV)0.61779485
Kurtosis144.94852
Mean0.98083343
Median Absolute Deviation (MAD)0.2
Skewness9.9946306
Sum972319.8
Variance0.36718006
MonotonicityNot monotonic
2023-12-18T13:28:05.828427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 201412
20.3%
1.2 188455
19.0%
1.5 121710
12.3%
0.9 105293
10.6%
0.8 99911
10.1%
0.7 83747
8.4%
0.6 53643
 
5.4%
0.5 51895
 
5.2%
0.4 30743
 
3.1%
0.3 20387
 
2.1%
Other values (14) 34124
 
3.4%
ValueCountFrequency (%)
0.1 9503
 
1.0%
0.2 12255
 
1.2%
0.3 20387
 
2.1%
0.4 30743
 
3.1%
0.5 51895
 
5.2%
0.6 53643
 
5.4%
0.7 83747
8.4%
0.8 99911
10.1%
0.9 105293
10.6%
1 201412
20.3%
ValueCountFrequency (%)
9.9 3118
 
0.3%
2.5 7
 
< 0.1%
2.2 2
 
< 0.1%
2.1 3
 
< 0.1%
2 8451
 
0.9%
1.9 32
 
< 0.1%
1.8 25
 
< 0.1%
1.7 14
 
< 0.1%
1.6 371
 
< 0.1%
1.5 121710
12.3%

sight_right
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.97842785
Minimum0.1
Maximum9.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 MiB
2023-12-18T13:28:05.869363image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.4
Q10.7
median1
Q31.2
95-th percentile1.5
Maximum9.9
Range9.8
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.60477905
Coefficient of variation (CV)0.61811307
Kurtosis145.92166
Mean0.97842785
Median Absolute Deviation (MAD)0.2
Skewness10.033672
Sum969935.1
Variance0.36575769
MonotonicityNot monotonic
2023-12-18T13:28:05.910427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 204488
20.6%
1.2 187261
18.9%
1.5 120614
12.2%
0.9 106182
10.7%
0.8 98777
10.0%
0.7 84164
8.5%
0.6 53238
 
5.4%
0.5 50803
 
5.1%
0.4 31318
 
3.2%
0.3 20090
 
2.0%
Other values (14) 34385
 
3.5%
ValueCountFrequency (%)
0.1 10027
 
1.0%
0.2 13001
 
1.3%
0.3 20090
 
2.0%
0.4 31318
 
3.2%
0.5 50803
 
5.1%
0.6 53238
 
5.4%
0.7 84164
8.5%
0.8 98777
10.0%
0.9 106182
10.7%
1 204488
20.6%
ValueCountFrequency (%)
9.9 3111
 
0.3%
2.5 10
 
< 0.1%
2.2 1
 
< 0.1%
2.1 10
 
< 0.1%
2 7363
 
0.7%
1.9 21
 
< 0.1%
1.8 32
 
< 0.1%
1.7 24
 
< 0.1%
1.6 390
 
< 0.1%
1.5 120614
12.2%

hear_left
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.1 MiB
1.0
960098 
2.0
 
31222

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2973960
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 960098
96.9%
2.0 31222
 
3.1%

Length

2023-12-18T13:28:05.950028image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-18T13:28:05.987288image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 960098
96.9%
2.0 31222
 
3.1%

Most occurring characters

ValueCountFrequency (%)
. 991320
33.3%
0 991320
33.3%
1 960098
32.3%
2 31222
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1982640
66.7%
Other Punctuation 991320
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 991320
50.0%
1 960098
48.4%
2 31222
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 991320
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2973960
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 991320
33.3%
0 991320
33.3%
1 960098
32.3%
2 31222
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2973960
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 991320
33.3%
0 991320
33.3%
1 960098
32.3%
2 31222
 
1.0%

hear_right
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.1 MiB
1.0
961109 
2.0
 
30211

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2973960
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 961109
97.0%
2.0 30211
 
3.0%

Length

2023-12-18T13:28:06.017286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-18T13:28:06.053426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 961109
97.0%
2.0 30211
 
3.0%

Most occurring characters

ValueCountFrequency (%)
. 991320
33.3%
0 991320
33.3%
1 961109
32.3%
2 30211
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1982640
66.7%
Other Punctuation 991320
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 991320
50.0%
1 961109
48.5%
2 30211
 
1.5%
Other Punctuation
ValueCountFrequency (%)
. 991320
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2973960
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 991320
33.3%
0 991320
33.3%
1 961109
32.3%
2 30211
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2973960
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 991320
33.3%
0 991320
33.3%
1 961109
32.3%
2 30211
 
1.0%

SBP
Real number (ℝ)

Distinct171
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.43236
Minimum67
Maximum273
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 MiB
2023-12-18T13:28:06.091242image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum67
5-th percentile100
Q1112
median120
Q3131
95-th percentile148
Maximum273
Range206
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.543083
Coefficient of variation (CV)0.11878463
Kurtosis0.9965739
Mean122.43236
Median Absolute Deviation (MAD)10
Skewness0.48203506
Sum1.2136965 × 108
Variance211.50126
MonotonicityNot monotonic
2023-12-18T13:28:06.137427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 78780
 
7.9%
110 72193
 
7.3%
130 71709
 
7.2%
118 40078
 
4.0%
100 30828
 
3.1%
138 24425
 
2.5%
119 24166
 
2.4%
128 23765
 
2.4%
124 22224
 
2.2%
116 22177
 
2.2%
Other values (161) 580975
58.6%
ValueCountFrequency (%)
67 1
 
< 0.1%
70 3
 
< 0.1%
72 1
 
< 0.1%
73 4
 
< 0.1%
74 3
 
< 0.1%
75 8
< 0.1%
76 7
< 0.1%
77 6
< 0.1%
78 11
< 0.1%
79 6
< 0.1%
ValueCountFrequency (%)
273 1
< 0.1%
270 1
< 0.1%
255 1
< 0.1%
253 1
< 0.1%
244 1
< 0.1%
241 1
< 0.1%
240 1
< 0.1%
238 1
< 0.1%
236 1
< 0.1%
235 1
< 0.1%

DBP
Real number (ℝ)

Distinct127
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.052549
Minimum32
Maximum185
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 MiB
2023-12-18T13:28:06.184884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile60
Q170
median76
Q382
95-th percentile92
Maximum185
Range153
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.8893341
Coefficient of variation (CV)0.13003291
Kurtosis0.89146188
Mean76.052549
Median Absolute Deviation (MAD)6
Skewness0.40001311
Sum75392413
Variance97.798928
MonotonicityNot monotonic
2023-12-18T13:28:06.232015image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 123151
 
12.4%
70 111694
 
11.3%
78 44624
 
4.5%
60 41252
 
4.2%
72 33644
 
3.4%
75 32574
 
3.3%
76 31976
 
3.2%
74 31773
 
3.2%
82 27195
 
2.7%
90 25959
 
2.6%
Other values (117) 487478
49.2%
ValueCountFrequency (%)
32 1
 
< 0.1%
33 1
 
< 0.1%
34 1
 
< 0.1%
36 2
 
< 0.1%
37 3
 
< 0.1%
38 1
 
< 0.1%
39 3
 
< 0.1%
40 14
< 0.1%
41 7
< 0.1%
42 12
< 0.1%
ValueCountFrequency (%)
185 1
< 0.1%
181 1
< 0.1%
180 1
< 0.1%
170 1
< 0.1%
164 1
< 0.1%
163 1
< 0.1%
160 2
< 0.1%
156 2
< 0.1%
154 2
< 0.1%
153 2
< 0.1%

BLDS
Real number (ℝ)

Distinct498
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.4243
Minimum25
Maximum852
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 MiB
2023-12-18T13:28:06.284011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile79
Q188
median96
Q3105
95-th percentile137
Maximum852
Range827
Interquartile range (IQR)17

Descriptive statistics

Standard deviation24.179852
Coefficient of variation (CV)0.24077689
Kurtosis40.472198
Mean100.4243
Median Absolute Deviation (MAD)8
Skewness4.6174953
Sum99552622
Variance584.66522
MonotonicityNot monotonic
2023-12-18T13:28:06.330942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93 35243
 
3.6%
92 35226
 
3.6%
95 35190
 
3.5%
94 35173
 
3.5%
91 34388
 
3.5%
96 33811
 
3.4%
90 33754
 
3.4%
97 32980
 
3.3%
89 32178
 
3.2%
98 31902
 
3.2%
Other values (488) 651475
65.7%
ValueCountFrequency (%)
25 1
 
< 0.1%
30 1
 
< 0.1%
32 1
 
< 0.1%
33 2
< 0.1%
34 2
< 0.1%
36 2
< 0.1%
37 1
 
< 0.1%
38 4
< 0.1%
39 1
 
< 0.1%
40 1
 
< 0.1%
ValueCountFrequency (%)
852 1
< 0.1%
801 1
< 0.1%
800 1
< 0.1%
784 1
< 0.1%
769 1
< 0.1%
741 1
< 0.1%
685 1
< 0.1%
663 1
< 0.1%
638 1
< 0.1%
629 2
< 0.1%

tot_chole
Real number (ℝ)

Distinct474
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean195.55677
Minimum30
Maximum2344
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 MiB
2023-12-18T13:28:06.378179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile137
Q1169
median193
Q3219
95-th percentile261
Maximum2344
Range2314
Interquartile range (IQR)50

Descriptive statistics

Standard deviation38.660092
Coefficient of variation (CV)0.19769243
Kurtosis49.464044
Mean195.55677
Median Absolute Deviation (MAD)25
Skewness1.5569267
Sum1.9385934 × 108
Variance1494.6027
MonotonicityNot monotonic
2023-12-18T13:28:06.425690image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199 11077
 
1.1%
184 10871
 
1.1%
189 10857
 
1.1%
190 10825
 
1.1%
188 10795
 
1.1%
197 10775
 
1.1%
192 10746
 
1.1%
187 10746
 
1.1%
196 10723
 
1.1%
186 10717
 
1.1%
Other values (464) 883188
89.1%
ValueCountFrequency (%)
30 1
 
< 0.1%
45 1
 
< 0.1%
54 1
 
< 0.1%
55 1
 
< 0.1%
57 3
< 0.1%
58 1
 
< 0.1%
59 1
 
< 0.1%
60 1
 
< 0.1%
62 1
 
< 0.1%
63 2
< 0.1%
ValueCountFrequency (%)
2344 1
< 0.1%
2196 1
< 0.1%
2067 1
< 0.1%
2046 1
< 0.1%
2033 1
< 0.1%
1815 1
< 0.1%
1736 1
< 0.1%
1619 1
< 0.1%
1605 1
< 0.1%
1575 1
< 0.1%

HDL_chole
Real number (ℝ)

Distinct223
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.936984
Minimum1
Maximum8110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 MiB
2023-12-18T13:28:06.474384image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile36
Q146
median55
Q366
95-th percentile84
Maximum8110
Range8109
Interquartile range (IQR)20

Descriptive statistics

Standard deviation17.238578
Coefficient of variation (CV)0.30276592
Kurtosis48094.305
Mean56.936984
Median Absolute Deviation (MAD)10
Skewness104.57855
Sum56442771
Variance297.16858
MonotonicityNot monotonic
2023-12-18T13:28:06.525942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 29602
 
3.0%
52 28333
 
2.9%
53 28323
 
2.9%
51 28124
 
2.8%
54 27952
 
2.8%
49 27868
 
2.8%
48 27427
 
2.8%
55 27091
 
2.7%
56 26827
 
2.7%
47 26475
 
2.7%
Other values (213) 713298
72.0%
ValueCountFrequency (%)
1 3
 
< 0.1%
2 7
< 0.1%
3 3
 
< 0.1%
4 5
 
< 0.1%
5 2
 
< 0.1%
6 6
 
< 0.1%
7 12
< 0.1%
8 6
 
< 0.1%
9 11
< 0.1%
10 15
< 0.1%
ValueCountFrequency (%)
8110 1
< 0.1%
1206 1
< 0.1%
933 1
< 0.1%
797 1
< 0.1%
727 1
< 0.1%
701 1
< 0.1%
697 1
< 0.1%
677 1
< 0.1%
658 1
< 0.1%
636 1
< 0.1%

LDL_chole
Real number (ℝ)

Distinct432
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.03743
Minimum1
Maximum5119
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 MiB
2023-12-18T13:28:06.578985image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile60
Q189
median111
Q3135
95-th percentile172
Maximum5119
Range5118
Interquartile range (IQR)46

Descriptive statistics

Standard deviation35.842938
Coefficient of variation (CV)0.31708911
Kurtosis481.28899
Mean113.03743
Median Absolute Deviation (MAD)23
Skewness5.2518298
Sum1.1205626 × 108
Variance1284.7162
MonotonicityNot monotonic
2023-12-18T13:28:06.630699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
109 11823
 
1.2%
104 11795
 
1.2%
107 11782
 
1.2%
110 11773
 
1.2%
102 11738
 
1.2%
112 11656
 
1.2%
115 11631
 
1.2%
108 11611
 
1.2%
105 11607
 
1.2%
106 11597
 
1.2%
Other values (422) 874307
88.2%
ValueCountFrequency (%)
1 81
< 0.1%
2 13
 
< 0.1%
3 13
 
< 0.1%
4 11
 
< 0.1%
5 20
 
< 0.1%
6 23
 
< 0.1%
7 29
 
< 0.1%
8 40
< 0.1%
9 31
 
< 0.1%
10 39
< 0.1%
ValueCountFrequency (%)
5119 1
< 0.1%
2254 1
< 0.1%
2114 1
< 0.1%
2111 1
< 0.1%
2043 1
< 0.1%
2026 1
< 0.1%
1933 1
< 0.1%
1798 1
< 0.1%
1750 1
< 0.1%
1696 1
< 0.1%

triglyceride
Real number (ℝ)

Distinct1657
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.14003
Minimum1
Maximum9490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 MiB
2023-12-18T13:28:06.681259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile46
Q173
median106
Q3159
95-th percentile297
Maximum9490
Range9489
Interquartile range (IQR)86

Descriptive statistics

Standard deviation102.19476
Coefficient of variation (CV)0.77338231
Kurtosis175.40489
Mean132.14003
Median Absolute Deviation (MAD)39
Skewness6.5297759
Sum1.3099306 × 108
Variance10443.769
MonotonicityNot monotonic
2023-12-18T13:28:06.830301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72 8236
 
0.8%
78 8207
 
0.8%
79 8178
 
0.8%
69 8139
 
0.8%
70 8131
 
0.8%
76 8122
 
0.8%
68 8120
 
0.8%
82 8101
 
0.8%
75 8096
 
0.8%
77 8095
 
0.8%
Other values (1647) 909895
91.8%
ValueCountFrequency (%)
1 4
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 2
 
< 0.1%
5 2
 
< 0.1%
6 2
 
< 0.1%
7 10
< 0.1%
8 7
< 0.1%
9 11
< 0.1%
10 8
< 0.1%
ValueCountFrequency (%)
9490 1
< 0.1%
6430 1
< 0.1%
6173 1
< 0.1%
5236 1
< 0.1%
4164 1
< 0.1%
4000 1
< 0.1%
3858 1
< 0.1%
3848 1
< 0.1%
3830 1
< 0.1%
3771 1
< 0.1%

hemoglobin
Real number (ℝ)

Distinct190
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.22981
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 MiB
2023-12-18T13:28:06.877432image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11.7
Q113.2
median14.3
Q315.4
95-th percentile16.6
Maximum25
Range24
Interquartile range (IQR)2.2

Descriptive statistics

Standard deviation1.5849241
Coefficient of variation (CV)0.11138055
Kurtosis0.71141536
Mean14.22981
Median Absolute Deviation (MAD)1.1
Skewness-0.38398384
Sum14106296
Variance2.5119845
MonotonicityNot monotonic
2023-12-18T13:28:06.928478image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.5 23297
 
2.4%
14 23108
 
2.3%
13.6 23092
 
2.3%
13.4 22946
 
2.3%
13.8 22779
 
2.3%
13.3 22734
 
2.3%
13.9 22635
 
2.3%
15 22598
 
2.3%
13.7 22591
 
2.3%
14.8 22181
 
2.2%
Other values (180) 763359
77.0%
ValueCountFrequency (%)
1 3
< 0.1%
2.8 1
 
< 0.1%
3.7 3
< 0.1%
3.8 1
 
< 0.1%
3.9 3
< 0.1%
4 4
< 0.1%
4.1 2
< 0.1%
4.2 4
< 0.1%
4.3 3
< 0.1%
4.4 2
< 0.1%
ValueCountFrequency (%)
25 2
< 0.1%
24.2 1
< 0.1%
23.9 1
< 0.1%
23.6 1
< 0.1%
23.3 1
< 0.1%
22.7 1
< 0.1%
22.1 1
< 0.1%
22 1
< 0.1%
21.8 1
< 0.1%
21.7 2
< 0.1%

urine_protein
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0942208
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 MiB
2023-12-18T13:28:06.967843image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4377188
Coefficient of variation (CV)0.40002785
Kurtosis36.901752
Mean1.0942208
Median Absolute Deviation (MAD)0
Skewness5.6726644
Sum1084723
Variance0.19159775
MonotonicityNot monotonic
2023-12-18T13:28:07.001073image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 935153
94.3%
2 30848
 
3.1%
3 16403
 
1.7%
4 6427
 
0.6%
5 1977
 
0.2%
6 512
 
0.1%
ValueCountFrequency (%)
1 935153
94.3%
2 30848
 
3.1%
3 16403
 
1.7%
4 6427
 
0.6%
5 1977
 
0.2%
6 512
 
0.1%
ValueCountFrequency (%)
6 512
 
0.1%
5 1977
 
0.2%
4 6427
 
0.6%
3 16403
 
1.7%
2 30848
 
3.1%
1 935153
94.3%

serum_creatinine
Real number (ℝ)

Distinct183
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.86046665
Minimum0.1
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 MiB
2023-12-18T13:28:07.045456image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.6
Q10.7
median0.8
Q31
95-th percentile1.2
Maximum98
Range97.9
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.48053586
Coefficient of variation (CV)0.5584596
Kurtosis19089.466
Mean0.86046665
Median Absolute Deviation (MAD)0.1
Skewness111.0212
Sum852997.8
Variance0.23091471
MonotonicityNot monotonic
2023-12-18T13:28:07.095118image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8 194897
19.7%
0.9 180618
18.2%
0.7 164290
16.6%
1 140739
14.2%
0.6 109235
11.0%
1.1 86353
8.7%
1.2 40744
 
4.1%
0.5 38930
 
3.9%
1.3 15159
 
1.5%
0.4 6050
 
0.6%
Other values (173) 14305
 
1.4%
ValueCountFrequency (%)
0.1 425
 
< 0.1%
0.2 99
 
< 0.1%
0.3 597
 
0.1%
0.4 6050
 
0.6%
0.5 38930
 
3.9%
0.6 109235
11.0%
0.7 164290
16.6%
0.8 194897
19.7%
0.9 180618
18.2%
1 140739
14.2%
ValueCountFrequency (%)
98 2
< 0.1%
96 2
< 0.1%
95 1
< 0.1%
94 1
< 0.1%
93 1
< 0.1%
87 1
< 0.1%
85 1
< 0.1%
81 1
< 0.1%
80 1
< 0.1%
79 1
< 0.1%

SGOT_AST
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct568
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.989424
Minimum1
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 MiB
2023-12-18T13:28:07.145634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15
Q119
median23
Q328
95-th percentile46
Maximum9999
Range9998
Interquartile range (IQR)9

Descriptive statistics

Standard deviation23.493668
Coefficient of variation (CV)0.90397032
Kurtosis50431.549
Mean25.989424
Median Absolute Deviation (MAD)5
Skewness150.4902
Sum25763836
Variance551.95244
MonotonicityNot monotonic
2023-12-18T13:28:07.196628image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 64899
 
6.5%
21 63843
 
6.4%
19 62843
 
6.3%
22 61195
 
6.2%
18 58422
 
5.9%
23 57641
 
5.8%
24 52473
 
5.3%
17 50159
 
5.1%
25 47304
 
4.8%
26 41665
 
4.2%
Other values (558) 430876
43.5%
ValueCountFrequency (%)
1 14
 
< 0.1%
2 19
 
< 0.1%
3 16
 
< 0.1%
4 28
 
< 0.1%
5 39
 
< 0.1%
6 70
 
< 0.1%
7 131
 
< 0.1%
8 297
 
< 0.1%
9 580
 
0.1%
10 1708
0.2%
ValueCountFrequency (%)
9999 1
< 0.1%
7000 2
< 0.1%
3742 1
< 0.1%
3440 1
< 0.1%
3235 1
< 0.1%
2670 1
< 0.1%
1962 1
< 0.1%
1911 1
< 0.1%
1870 1
< 0.1%
1686 1
< 0.1%

SGOT_ALT
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct594
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.755148
Minimum1
Maximum7210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 MiB
2023-12-18T13:28:07.256787image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q115
median20
Q329
95-th percentile59
Maximum7210
Range7209
Interquartile range (IQR)14

Descriptive statistics

Standard deviation26.30891
Coefficient of variation (CV)1.021501
Kurtosis8615.7629
Mean25.755148
Median Absolute Deviation (MAD)7
Skewness50.038404
Sum25531593
Variance692.15873
MonotonicityNot monotonic
2023-12-18T13:28:07.312101image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 47840
 
4.8%
16 47260
 
4.8%
14 47173
 
4.8%
17 46075
 
4.6%
18 44335
 
4.5%
13 44022
 
4.4%
19 41893
 
4.2%
12 40938
 
4.1%
20 38803
 
3.9%
21 36255
 
3.7%
Other values (584) 556726
56.2%
ValueCountFrequency (%)
1 31
 
< 0.1%
2 60
 
< 0.1%
3 186
 
< 0.1%
4 567
 
0.1%
5 1636
 
0.2%
6 3259
 
0.3%
7 6591
 
0.7%
8 12212
 
1.2%
9 18937
1.9%
10 31707
3.2%
ValueCountFrequency (%)
7210 1
< 0.1%
4633 1
< 0.1%
3807 1
< 0.1%
3517 1
< 0.1%
3307 1
< 0.1%
2981 1
< 0.1%
2698 1
< 0.1%
2535 1
< 0.1%
2530 1
< 0.1%
2309 1
< 0.1%

gamma_GTP
Real number (ℝ)

Distinct940
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.136152
Minimum1
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 MiB
2023-12-18T13:28:07.367786image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q116
median23
Q339
95-th percentile105
Maximum999
Range998
Interquartile range (IQR)23

Descriptive statistics

Standard deviation50.423811
Coefficient of variation (CV)1.3578093
Kurtosis97.046238
Mean37.136152
Median Absolute Deviation (MAD)9
Skewness7.718657
Sum36813810
Variance2542.5608
MonotonicityNot monotonic
2023-12-18T13:28:07.417299image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 41157
 
4.2%
15 40932
 
4.1%
13 39919
 
4.0%
16 39750
 
4.0%
17 37763
 
3.8%
12 36681
 
3.7%
18 35756
 
3.6%
19 33417
 
3.4%
11 31564
 
3.2%
20 31362
 
3.2%
Other values (930) 623019
62.8%
ValueCountFrequency (%)
1 16
 
< 0.1%
2 31
 
< 0.1%
3 206
 
< 0.1%
4 239
 
< 0.1%
5 621
 
0.1%
6 1623
 
0.2%
7 3493
 
0.4%
8 8202
 
0.8%
9 14181
1.4%
10 25642
2.6%
ValueCountFrequency (%)
999 239
< 0.1%
998 1
 
< 0.1%
997 1
 
< 0.1%
996 2
 
< 0.1%
994 2
 
< 0.1%
993 4
 
< 0.1%
992 2
 
< 0.1%
991 1
 
< 0.1%
990 5
 
< 0.1%
989 2
 
< 0.1%

SMK_stat_type_cd
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.1 MiB
1.0
602431 
3.0
213945 
2.0
174944 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2973960
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row3.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 602431
60.8%
3.0 213945
 
21.6%
2.0 174944
 
17.6%

Length

2023-12-18T13:28:07.459225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-18T13:28:07.501124image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 602431
60.8%
3.0 213945
 
21.6%
2.0 174944
 
17.6%

Most occurring characters

ValueCountFrequency (%)
. 991320
33.3%
0 991320
33.3%
1 602431
20.3%
3 213945
 
7.2%
2 174944
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1982640
66.7%
Other Punctuation 991320
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 991320
50.0%
1 602431
30.4%
3 213945
 
10.8%
2 174944
 
8.8%
Other Punctuation
ValueCountFrequency (%)
. 991320
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2973960
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 991320
33.3%
0 991320
33.3%
1 602431
20.3%
3 213945
 
7.2%
2 174944
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2973960
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 991320
33.3%
0 991320
33.3%
1 602431
20.3%
3 213945
 
7.2%
2 174944
 
5.9%

DRK_YN
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.5 MiB
False
495844 
True
495476 
ValueCountFrequency (%)
False 495844
50.0%
True 495476
50.0%
2023-12-18T13:28:07.538201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Interactions

2023-12-18T13:28:00.172798image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:26.909977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:28.736198image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:30.591405image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:32.560701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:34.394364image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:36.506869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:38.490006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:40.416810image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:42.490873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:44.359818image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:46.145014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:47.989307image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:49.747469image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:51.473700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:53.365565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:55.074770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:56.674813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:58.455265image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:28:00.267527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:27.014482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:28.827941image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:30.692656image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:32.658150image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:34.498809image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:36.612715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:38.595888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:40.524994image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:42.586817image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:44.455758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:46.236501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:48.078598image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:49.836836image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:51.564524image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:53.452454image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:55.158033image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:56.764814image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:58.543947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:28:00.359896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:27.111449image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:28.919733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:30.795070image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:32.754131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:34.601111image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:36.725040image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:38.689258image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:40.625401image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:42.680397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:44.549443image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:46.334513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:48.171586image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:49.925529image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:51.654174image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:53.542010image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:55.241492image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:56.850792image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:58.631119image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:28:00.454094image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:27.209632image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:29.013624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:30.897368image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:32.848354image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:34.709676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:36.831133image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:38.785665image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:40.727047image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:42.779741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:44.647844image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:46.428146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:48.265889image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:50.021518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:51.754732image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:53.633635image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:55.327104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:56.936995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:58.720888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:28:00.546053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:27.308204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:29.103041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:30.993790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:32.942513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:34.806007image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:36.934739image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:38.879015image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:40.831151image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:42.874672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:44.737692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:46.516416image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:48.364946image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:50.110506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:51.849831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:53.724104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:55.413181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:57.026955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:58.819408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:28:00.637812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:27.402900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:29.193690image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:31.089196image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:33.040891image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:34.907900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:37.036904image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:38.975957image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:40.932910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:42.975493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:44.829897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:46.604469image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:48.461045image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:50.224308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:51.947128image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:53.815734image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:55.497738image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:57.115537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:58.912492image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:28:00.729408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:27.500360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:29.281746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:31.182085image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:33.137190image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:35.008029image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:37.139896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:39.072932image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:41.039533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:43.069543image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:44.922660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:46.689681image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:48.552874image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:50.311898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:52.043300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:53.905825image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:55.581858image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:57.202055image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:59.003089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:28:00.817990image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:27.593318image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:29.368560image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:31.283029image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:33.224923image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:35.106924image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:37.238449image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:39.177483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:41.141483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:43.169040image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:45.016789image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:46.784258image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:48.642496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:50.399556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:52.135686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:53.992382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:55.661867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:57.288959image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:59.091901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:28:00.914285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:27.690817image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:29.459471image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:31.393295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:33.317464image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:35.211473image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:37.347762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-12-18T13:27:56.592016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:27:58.371828image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-18T13:28:00.077338image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-12-18T13:28:07.584589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ageheightweightwaistlinesight_leftsight_rightSBPDBPBLDStot_choleHDL_choleLDL_choletriglyceridehemoglobinurine_proteinserum_creatinineSGOT_ASTSGOT_ALTgamma_GTPsexhear_lefthear_rightSMK_stat_type_cdDRK_YN
age1.000-0.385-0.1730.165-0.387-0.3800.2620.1190.2540.024-0.1140.0390.127-0.1800.0240.0010.2070.0670.0590.1270.2410.2350.1440.291
height-0.3851.0000.6800.3420.2730.2750.0560.1170.045-0.020-0.190-0.0090.1500.5850.0090.4620.0830.2470.3230.7440.0990.1000.3610.375
weight-0.1730.6801.0000.7810.1710.1720.2640.2770.2020.065-0.3480.0790.3520.5460.0250.4190.2130.4410.4620.5880.0500.0510.2820.260
waistline0.1650.3420.7811.000-0.012-0.0090.3500.3030.2860.079-0.3810.0870.4150.3940.0420.3120.2780.4530.4710.0430.0030.0030.0230.016
sight_left-0.3870.2730.171-0.0121.0000.717-0.091-0.013-0.0850.0200.0090.017-0.0080.181-0.0220.087-0.0410.0400.0440.1430.0780.0750.0700.132
sight_right-0.3800.2750.172-0.0090.7171.000-0.088-0.010-0.0840.0210.0050.018-0.0060.183-0.0230.090-0.0380.0420.0460.1470.0760.0760.0710.132
SBP0.2620.0560.2640.350-0.091-0.0881.0000.7250.2430.071-0.1410.0390.2510.1840.0400.1320.2030.2320.2780.2030.0530.0550.0910.050
DBP0.1190.1170.2770.303-0.013-0.0100.7251.0000.1920.112-0.1170.0720.2470.2490.0310.1370.1830.2320.2880.1940.0070.0080.0980.093
BLDS0.2540.0450.2020.286-0.085-0.0840.2430.1921.0000.046-0.1510.0080.2630.1320.0540.1270.1480.2280.2790.1150.0460.0470.0750.014
tot_chole0.024-0.0200.0650.0790.0200.0210.0710.1120.0461.0000.1580.8870.2750.115-0.0080.0250.1030.1260.1560.0160.0030.0050.0060.008
HDL_chole-0.114-0.190-0.348-0.3810.0090.005-0.141-0.117-0.1510.1581.000-0.042-0.469-0.241-0.024-0.227-0.104-0.249-0.2220.0010.0000.0000.0000.000
LDL_chole0.039-0.0090.0790.0870.0170.0180.0390.0720.0080.887-0.0421.0000.1090.106-0.0140.0440.0550.0900.0740.0000.0000.0030.0020.000
triglyceride0.1270.1500.3520.415-0.008-0.0060.2510.2470.2630.275-0.4690.1091.0000.2960.0300.1890.2230.3610.4490.0290.0010.0000.0240.025
hemoglobin-0.1800.5850.5460.3940.1810.1830.1840.2490.1320.115-0.2410.1060.2961.0000.0170.4580.2430.4180.4680.6210.0350.0360.3110.280
urine_protein0.0240.0090.0250.042-0.022-0.0230.0400.0310.054-0.008-0.024-0.0140.0300.0171.0000.0370.0250.0210.0380.0200.0210.0190.0140.017
serum_creatinine0.0010.4620.4190.3120.0870.0900.1320.1370.1270.025-0.2270.0440.1890.4580.0371.0000.1820.2460.3210.0080.0020.0000.0030.006
SGOT_AST0.2070.0830.2130.278-0.041-0.0380.2030.1830.1480.103-0.1040.0550.2230.2430.0250.1821.0000.7310.4630.0010.0000.0000.0010.001
SGOT_ALT0.0670.2470.4410.4530.0400.0420.2320.2320.2280.126-0.2490.0900.3610.4180.0210.2460.7311.0000.6190.0020.0000.0000.0020.000
gamma_GTP0.0590.3230.4620.4710.0440.0460.2780.2880.2790.156-0.2220.0740.4490.4680.0380.3210.4630.6191.0000.1640.0060.0070.1210.153
sex0.1270.7440.5880.0430.1430.1470.2030.1940.1150.0160.0010.0000.0290.6210.0200.0080.0010.0020.1641.0000.0030.0000.6430.369
hear_left0.2410.0990.0500.0030.0780.0760.0530.0070.0460.0030.0000.0000.0010.0350.0210.0020.0000.0000.0060.0031.0000.5370.0320.058
hear_right0.2350.1000.0510.0030.0750.0760.0550.0080.0470.0050.0000.0030.0000.0360.0190.0000.0000.0000.0070.0000.5371.0000.0310.058
SMK_stat_type_cd0.1440.3610.2820.0230.0700.0710.0910.0980.0750.0060.0000.0020.0240.3110.0140.0030.0010.0020.1210.6430.0320.0311.0000.365
DRK_YN0.2910.3750.2600.0160.1320.1320.0500.0930.0140.0080.0000.0000.0250.2800.0170.0060.0010.0000.1530.3690.0580.0580.3651.000

Missing values

2023-12-18T13:28:02.074302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-18T13:28:02.961914image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

sexageheightweightwaistlinesight_leftsight_righthear_lefthear_rightSBPDBPBLDStot_choleHDL_choleLDL_choletriglyceridehemoglobinurine_proteinserum_creatinineSGOT_ASTSGOT_ALTgamma_GTPSMK_stat_type_cdDRK_YN
0Male351707590.01.01.01.01.0120.080.099.0193.048.0126.092.017.11.01.021.035.040.01.0Y
1Male301808089.00.91.21.01.0130.082.0106.0228.055.0148.0121.015.81.00.920.036.027.03.0N
2Male401657591.01.21.51.01.0120.070.098.0136.041.074.0104.015.81.00.947.032.068.01.0N
3Male501758091.01.51.21.01.0145.087.095.0201.076.0104.0106.017.61.01.129.034.018.01.0N
4Male501656080.01.01.21.01.0138.082.0101.0199.061.0117.0104.013.81.00.819.012.025.01.0N
5Male501655575.01.21.51.01.0142.092.099.0218.077.095.0232.013.83.00.829.040.037.03.0Y
6Female451505569.00.50.41.01.0101.058.089.0196.066.0115.075.012.31.00.819.012.012.01.0N
7Male351756584.21.21.01.01.0132.080.094.0185.058.0107.0101.014.41.00.818.018.035.03.0Y
8Male551707584.01.20.91.01.0145.085.0104.0217.056.0141.0100.015.11.00.832.023.026.01.0Y
9Male401757582.01.51.51.01.0132.0105.0100.0195.060.0118.083.013.91.00.921.038.016.02.0Y
sexageheightweightwaistlinesight_leftsight_righthear_lefthear_rightSBPDBPBLDStot_choleHDL_choleLDL_choletriglyceridehemoglobinurine_proteinserum_creatinineSGOT_ASTSGOT_ALTgamma_GTPSMK_stat_type_cdDRK_YN
991336Male801706074.01.00.91.01.0139.083.0109.0171.075.084.057.012.01.01.218.011.015.02.0Y
991337Female351657081.01.01.01.01.0113.069.081.0173.063.092.088.013.31.00.720.017.012.01.0N
991338Male201756574.51.01.51.01.0105.070.087.0211.072.0120.092.015.41.00.825.026.050.02.0Y
991339Male701656078.00.90.81.01.0137.078.093.0167.057.089.0105.016.11.01.023.013.032.01.0Y
991340Female501505072.61.01.01.01.0116.074.0108.0178.048.0105.0125.015.21.00.828.026.029.01.0N
991341Male451758092.11.51.51.01.0114.080.088.0198.046.0125.0132.015.01.01.026.036.027.01.0N
991342Male351707586.01.01.51.01.0119.083.083.0133.040.084.045.015.81.01.114.017.015.01.0N
991343Female401555068.01.00.71.01.0110.070.090.0205.096.077.0157.014.31.00.830.027.017.03.0Y
991344Male251756072.01.51.01.01.0119.074.069.0122.038.073.053.014.51.00.821.014.017.01.0N
991345Male501607090.51.01.51.01.0133.079.099.0225.039.0153.0163.015.81.00.924.043.036.03.0Y